The KIR3DL1 gene is unique in terms of its diversity and expression patterns. It is the only KIR locus that encodes either inhibitory (KIR3DL1) or activating (KIR3DS1) allotypes. The inhibitory KIR3DL1 shows extensive polymorphism and its variation has functional significance in terms of cell surface expression levels and inhibitory capacity. KIR3DL1 allotypes have specificity for HLA-B (and sometimes HLA-A) molecules with the Bw4 serological motif. Staining with the KIR3DL1-specific mAb DX9 revealed that KIR3DL1 subtypes are expressed at different levels (high, low and null) on the NK cell surface, a phenomenon due at least in part to variation in the extracellular domains. Since DX9 blocks the interaction between KIR3DL1 and Bw4, enabling NK cells to kill Bw4+ targets, the quantity of KIR3DL1 on the cell surface could affect the inhibitory capacity of cells expressing these molecules. KIR3DL1 polymorphism also has direct influence on the recognition of Bw4 molecules with specific peptides sitting in their binding groove. We recently characterized nine KIR3DL1 alleles (*022, *028, *029, *033, *035, *051, *052, *053 and *054), of which four were identified for the first time in this study, and compared them to known alleles in phylogenetic analysis. Cell surface expression on natural killer cells could be determined for six of them using the KIR3DL1-specific antibody DX9. Four of the alleles were expressed at clearly detectable levels and two others showed exceptionally low levels of expression. Site directed mutagenesis demonstrated that single amino acid changes can result in either diminished or enhanced DX9 staining compared to the respective related KIR3DL1 allotypes. These data hint at the possibility of convergent evolution of KIR3DL1 phenotype variation, where the maintained variants represent a spectrum in terms of cell surface expression levels. They also build upon recent functional and disease association studies that highlight the biological significance of KIR3DL1 variation. Because KIR3DS1 has been implicated in a number of diseases, we also tested for the presence of KIR3DS1 variants that might affect its expression and activating capacity. Preliminary FACS analysis indicated that indeed some individuals with the KIR3DS1 allele showed no cell surface expression of the molecule. Sequencing analysis identified a variant with a complex deletion/substitution mutation in exon 4 (which encodes the D1 extracellular domain), resulting in a premature stop codon (KIR3DS1*049N). Subsequent genotyping of 3960 unrelated individuals was performed and frequencies of this allele across geographically distinct world populations were determined. The data indicate that KIR3DS1*049N is uncommon, arose on a single haplotype, and spread across geographically distinct populations. Historically, HLA typing was performed using low-resolution, antibody-based serological tests. However, higher-resolution HLA typing is now achievable using more modern, molecular (DNA-based) methods. High-resolution HLA typing plays a central role in many areas of immunology, such as in identifying immunogenetic risk factors for disease, in studying how the genomes of pathogens evolve in response to immune selection pressures, and also in vaccine design, where identification of HLA-restricted epitopes may be used to guide the selection of vaccine immunogens. Perhaps one of the most immediate applications is in direct medical decisions concerning the matching of stem cell transplant donors to unrelated recipients. However, high-resolution HLA typing is frequently unavailable due to its high cost or the inability to re-type historical data. Because of the great (and ever-increasing) number of HLA alleles (and thus growing list of ambiguous combinations), unambiguous HLA typing is costly, laborious, and limited to laboratories specializing in this work. Additionally, because the number of HLA alleles is constantly increasing, current methodologies which depend on the list of known alleles, require constant re-interpretation in light of newly discovered alleles. This re-interpretation can result in more ambiguity than originally thought. Perhaps even more importantly, it is often impossible to re-type historic samples that may have been typed using lower-resolution approaches. The practical consequence of these issues is that there is a large incongruence between the high-resolution HLA typing required for scientific investigations and the HLA data that is widely available. As such, any method which can help to increase resolution of HLA data, post-hoc and at low cost, will provide a greatly needed service to the scientific and clinical communities. In collaboration with others at Microsoft Research and Harvard medical school, we developed and evaluated a method for statistical, in silico refinement of ambiguous and/or low-resolution HLA data. The method, which requires an independent, high-resolution training data set drawn from the same population as the data to be refined, uses linkage disequilibrium in HLA haplotypes as well as four-digit allele frequency data to probabilistically refine HLA typings. This new methodology improves upon the Expectation-Maximization (EM)-based approaches currently used within the HLA community. The improvements are achieved by using a parsimonious parameterization for haplotype distributions and by smoothing the maximum likelihood (ML) solution. These improvements make it possible to scale the refinement to a larger number of alleles and loci in a more computationally efficient and stable manner. In addition, the method is able to incorporate ethnicity information (as HLA allele distributions vary widely according to race/ethnicity as well as geographic area), and demonstrate the potential utility of this experimentally. A tool based on the method has been made freely available for research purposes at http://microsoft.com/science. A number of statistical methods are widely used to describe allelic variation at specific genetic loci and its implication on the evolutionary history of these loci. Although the methods were developed primarily to study allelic variation at loci that are virtually always present in the genome, they are often applied to data of gene content variation (i.e. presence/absence of multiple homologous genes) at the KIR gene cluster. In collaboration with Dr. Rich Single at the University of Vermont, we have compared several statistical methods and measures (gene frequency, haplotype frequency, and linkage disequilibrium estimation) using KIR haplotypes that have been determined by segregation analysis from the Centre dEtude Polymorphisme Humain (CEPH) families. The availability of a family-based dataset containing information on KIR gene content variation allows us to obtain direct estimates of gene content, with respect to which estimates based on simple presence/absence information can be compared. We then applied these methods to a set of globally distributed populations in order to illustrate the challenges faced when inferring the joint effects of natural selection and demographic history on these immune related genes. Population-level studies provide an opportunity to corroborate results from other study designs, such as disease association studies, on [summary truncated at 7800 characters]